{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from PIL import Image\n",
    "from nets import nets_factory\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 批次大小\n",
    "BATCH_SIZE = 1\n",
    "# tf文件\n",
    "TFRECORD_TEST_FILE = 'captcha/test.tfrecord'\n",
    "CHAR_SET_LEN = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_and_decode(filename):\n",
    "    # 根据文件名生成一个队列\n",
    "    filename_queue = tf.train.string_input_producer([filename])\n",
    "    reader = tf.TFRecordReader()\n",
    "    # 返回文件名和文件\n",
    "    _, serialized_example = reader.read(filename_queue)   \n",
    "    features = tf.parse_single_example(serialized_example,\n",
    "                                       features={\n",
    "                                           'image' : tf.FixedLenFeature([], tf.string),\n",
    "                                           'label0': tf.FixedLenFeature([], tf.int64),\n",
    "                                           'label1': tf.FixedLenFeature([], tf.int64),\n",
    "                                           'label2': tf.FixedLenFeature([], tf.int64),\n",
    "                                           'label3': tf.FixedLenFeature([], tf.int64),\n",
    "                                       })\n",
    "    # 获取图片数据\n",
    "    image = tf.decode_raw(features['image'], tf.uint8)\n",
    "    # 没有经过预处理的灰度图\n",
    "    image_raw = tf.reshape(image, [224, 224])\n",
    "    # tf.train.shuffle_batch必须确定shape\n",
    "    image = tf.reshape(image, [224, 224])\n",
    "    # 图片预处理\n",
    "    image = tf.cast(image, tf.float32) / 255.0\n",
    "    image = tf.subtract(image, 0.5)\n",
    "    image = tf.multiply(image, 2.0)\n",
    "    # 获取label\n",
    "    label0 = tf.cast(features['label0'], tf.int32)\n",
    "    label1 = tf.cast(features['label1'], tf.int32)\n",
    "    label2 = tf.cast(features['label2'], tf.int32)\n",
    "    label3 = tf.cast(features['label3'], tf.int32)\n",
    "\n",
    "    return image, image_raw, label0, label1, label2, label3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from captcha/model/crack_captcha.model-1358\n"
     ]
    },
    {
     "data": {
      "image/png": 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6Le7/+9//AHTK0s1SbMdx9IyNRsPa5dntln/44YcAgBs3bryxtXs32Wq7+U5CT4EQYkGj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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [5] [8] [0] [7]\n",
      "predict: [5] [8] [0] [7]\n"
     ]
    },
    {
     "data": {
      "image/png": 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UwNapzKOjo/ua9HwcoKdACLGgUSCEWAy1fJAdguZEX7P8VtJmwN5TYQCs1uB+7q+ct1qt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\nBUKIBY0CIcSCRoEQYkGjQAix+H/86vdGTImWrAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [9] [0] [7] [2]\n",
      "predict: [9] [0] [7] [2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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3vPuzn/xGP87quucRZwpEZOCgQEQGDgpEZLg0OYXNzc2uZbOxWEzj5FQqhbW1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\nFIjIwEGBiAwcFIjIwEGBiAz/B7XwQRJ1qCSJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [2] [2] [7] [0]\n",
      "predict: [2] [2] [1] [0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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S65tvvtHKx59++glfffWVnqvruvo9SPWn3Nvs7CzGxsYAhO7tXkgfucaJiQlr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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [6] [4] [0] [1]\n",
      "predict: [6] [4] [0] [1]\n"
     ]
    },
    {
     "data": {
      "image/png": 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83ViAebK0uWdAe8zdTns1NrsHoB336BzPd5TiCvQUCCEWNAqEEItjKR+eP3+u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\nRoEQYkGjQAixoFEghFjQKBBCLP4fAj6adqV45skAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [2] [5] [5] [0]\n",
      "predict: [2] [5] [5] [0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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+uj1HsVKp4PHjxwCApaUleJ6nhVfq61KpJOHD21SrVelaVGVcFU5Uq1VtM1zD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\nRKT5Py61eWkmbf1dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [3] [9] [2] [2]\n",
      "predict: [3] [9] [7] [2]\n"
     ]
    },
    {
     "data": {
      "image/png": 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2Fu+++65ec/R1yRJwPp/X17a0tHRh8bZ5qMvjx48xMzOjy7dmdWksFoPjOHo4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\nsnBQICLL/wN6Sg/rCKSxKQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [1] [5] [7] [3]\n",
      "predict: [1] [5] [7] [3]\n"
     ]
    },
    {
     "data": {
      "image/png": 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sFjW9NpFIaIpytyEv02y7efMmFhcX8d///hdAMDBF0qir1aoVSstms1qyvJeU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\nHgqEEAseCoQQCx4KhBCL/wfrz86bWVmqiQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [9] [8] [7] [3]\n",
      "predict: [9] [8] [7] [9]\n"
     ]
    },
    {
     "data": {
      "image/png": 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ghFjQKBBCLPo+pmAOTN0tpuA4DvL5PKanp3uqJTiKqTiiVX3f39L2LVo+Ho/j9OnTAMJJ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\nhBALGgVCiAWNAiHEgkaBEGLx/wDA87nmuM4S7gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [5] [5] [8] [0]\n",
      "predict: [5] [5] [8] [0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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zH+e+JNVo/ja6uXjxonZxvivkdyFdjkclCIKeVb+HhZ4CIcSCRoEQYtE38uG4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\njQIhxOL/AU46Wt6hknc/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label: [6] [3] [1] [5]\n",
      "predict: [6] [3] [1] [5]\n"
     ]
    }
   ],
   "source": [
    "# 获取图片数据和标签\n",
    "image, image_raw, label0, label1, label2, label3 = read_and_decode(TFRECORD_TEST_FILE)\n",
    "\n",
    "#使用shuffle_batch可以随机打乱\n",
    "image_batch, image_raw_batch, label_batch0, label_batch1, label_batch2, label_batch3 = tf.train.shuffle_batch(\n",
    "        [image, image_raw, label0, label1, label2, label3], batch_size = BATCH_SIZE,\n",
    "        capacity = 1000, min_after_dequeue=200, num_threads=1)\n",
    "\n",
    "#定义网络结构\n",
    "train_network_fn = nets_factory.get_network_fn(\n",
    "    'alexnet_v2_captcha_multi',\n",
    "    num_classes=CHAR_SET_LEN,\n",
    "    weight_decay=0.0005,\n",
    "    is_training=False)\n",
    "\n",
    "# placeholder\n",
    "x = tf.placeholder(tf.float32, [None, 224, 224])  \n",
    "\n",
    "# inputs: a tensor of size [batch_size, height, width, channels]\n",
    "X = tf.reshape(x, [BATCH_SIZE, 224, 224, 1])\n",
    "# 数据输入网络得到输出值\n",
    "logits0,logits1,logits2,logits3,end_points = train_network_fn(X)\n",
    "\n",
    "# 预测值\n",
    "predict0 = tf.reshape(logits0, [-1, CHAR_SET_LEN])  \n",
    "predict0 = tf.argmax(predict0, 1)  \n",
    "\n",
    "predict1 = tf.reshape(logits1, [-1, CHAR_SET_LEN])  \n",
    "predict1 = tf.argmax(predict1, 1)  \n",
    "\n",
    "predict2 = tf.reshape(logits2, [-1, CHAR_SET_LEN])  \n",
    "predict2 = tf.argmax(predict2, 1)  \n",
    "\n",
    "predict3 = tf.reshape(logits3, [-1, CHAR_SET_LEN])  \n",
    "predict3 = tf.argmax(predict3, 1)  \n",
    "\n",
    "saver = tf.train.Saver()\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    saver.restore(sess,'captcha/model/crack_captcha.model-1358')\n",
    "    \n",
    "    coord = tf.train.Coordinator()\n",
    "    threads = tf.train.start_queue_runners(sess=sess, coord=coord)\n",
    "    \n",
    "    for i in range(10):\n",
    "        # 获取一个批次的数据和标签\n",
    "        b_image, b_image_raw, b_label0, b_label1 ,b_label2 ,b_label3 = sess.run([image_batch, \n",
    "                                                                    image_raw_batch, \n",
    "                                                                    label_batch0, \n",
    "                                                                    label_batch1, \n",
    "                                                                    label_batch2, \n",
    "                                                                    label_batch3])\n",
    "        # 显示图片\n",
    "        img=Image.fromarray(b_image_raw[0],'L')\n",
    "        plt.imshow(img)\n",
    "        plt.axis('off')\n",
    "        plt.show()\n",
    "        # 打印标签\n",
    "        print('label:',b_label0, b_label1 ,b_label2 ,b_label3)\n",
    "        # 预测\n",
    "        label0,label1,label2,label3 = sess.run([predict0,predict1,predict2,predict3], feed_dict={x: b_image})\n",
    "        # 打印预测值\n",
    "        print('predict:',label0,label1,label2,label3) \n",
    "    coord.request_stop()\n",
    "    coord.join(threads)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
